Predictive Biostatistical Modeling Of Uric Acid Levels Based On High-density Lipoprotein And Alanine Aminotransferase Using R

This study uses biostatistics and R syntax to analyze and model High-Density Lipoprotein (HDL), Alanine Aminotransferase (ALT), and Uric acid values. The work addresses the intricate relationships between these parameters to improve biological prediction accuracy. After Mardia’s test of multivariate normality, data normalization was done methodically to ensure variable comparability. A multiple linear regression model was used to develop a predictive model that estimated HDL and ALT contributions to Uric acid levels, revealing their relative importance. The regression model’s p values and contribution percentages showed that ALT affected Uric acid levels more than HDL.